MIT (Massachusetts Institute of Technology) articles

Given only a few frames of a video, humans can usually surmise what is happening and will happen on screen. If we see an early frame of stacked cans, a middle frame with a finger at the stack’s base, and a late frame showing the cans toppled over, we can guess that the finger knocked down the cans. Computers, however, struggle with this concept.

Humans have long been masters of dexterity, a skill that can largely be credited to the help of our eyes. Robots, meanwhile, are still catching up. Certainly there’s been some progress: For decades, robots in controlled environments like assembly lines have been able to pick up the same object over and over again. More recently, breakthroughs in computer vision have enabled robots to make basic distinctions between objects.

A novel encryption method devised by MIT researchers secures data used in online neural networks, without dramatically slowing their runtimes. This approach holds promise for using cloud-based neural networks for medical-image analysis and other applications that use sensitive data. Outsourcing machine learning is a rising trend in industry.

Researchers at MIT have created what may be the smallest robots yet that can sense their environment, store data, and even carry out computational tasks. These devices, which are about the size of a human egg cell, consist of tiny electronic circuits made of two-dimensional materials, piggybacking on minuscule particles called colloids. Colloids, which insoluble particles or molecules anywhere from a billionth to a millionth of a meter across, are so small they can stay suspended indefinitely in a liquid or even in air.

While exoskeletons may still feel like a product of science fiction, they are increasingly becoming a technological reality, and advancements are being made in bringing functioning exoskeletons into the world.

Getting robots to do things isn’t easy: Usually, scientists have to either explicitly program them or get them to understand how humans communicate via language. But what if we could control robots more intuitively, using just hand gestures and brainwaves? A system spearheaded by researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) aims to do exactly that, allowing users to instantly correct robot mistakes with nothing more than brain signals and the flick of a finger.

Have you ever plugged in a vacuum cleaner, only to have it turn off without warning before the job is done? Or perhaps your desk lamp works fine, until you turn on the air conditioner that’s plugged into the same power strip. These interruptions are likely 'nuisance trips,' in which a detector installed behind the wall trips an outlet’s electrical circuit when it senses something that could be an arc-fault — a potentially dangerous spark in the electric line.

X-ray vision has long seemed like a far-fetched sci-fi fantasy, but over the last decade a team led by Professor Dina Katabi from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has continually gotten us closer to seeing through walls. Their latest project, 'RF-Pose,' uses artificial intelligence (AI) to teach wireless devices to sense people’s postures and movement, even from the other side of a wall.

A technique developed by MIT physicists could someday provide a way to custom-design multilayered nanoparticles with desired properties, potentially for use in displays, cloaking systems, or biomedical devices. It may also help physicists tackle a variety of thorny research problems, in ways that could in some cases be orders of magnitude faster than existing methods.

For many people, household chores are a dreaded, inescapable part of life that we often put off or do with little care. But what if a robot assistant could help lighten the load? Recently, computer scientists have been working on teaching machines to do a wider range of tasks around the house.

MIT engineers have designed a robotic glider that can skim along the water’s surface, riding the wind like an albatross while also surfing the waves like a sailboat. In regions of high wind, the robot is designed to stay aloft, much like its avian counterpart. Where there are calmer winds, the robot can dip a keel into the water to ride like a highly efficient sailboat instead.

MIT has launched its Task Force on the Work of the Future, an Institute-wide effort to understand and shape the evolution of jobs during an age of innovation. The task force’s mission was announced in a letter to the MIT community by Provost Martin A. Schmidt. “The MIT Task Force on the Work of the Future takes as a guiding premise that addressing the social and human implications of technology should not be an afterthought".

Unpacking groceries is a straightforward albeit tedious task: You reach into a bag, feel around for an item, and pull it out. A quick glance will tell you what the item is and where it should be stored. Now engineers from MIT and Princeton University have developed a robotic system that may one day lend a hand with this household chore, as well as assist in other picking and sorting tasks, from organising products in a warehouse to clearing debris from a disaster zone.

Most recent advances in artificial-intelligence systems such as speech- or face-recognition programs have come courtesy of neural networks, densely interconnected meshes of simple information processors that learn to perform tasks by analysing huge sets of training data. But neural nets are large, and their computations are energy intensive, so they’re not very practical for handheld devices.

Three commercially released facial-analysis programs from major technology companies demonstrate both skin-type and gender biases, according to a new paper researchers from MIT and Stanford University will present later this month at the Conference on Fairness, Accountability, and Transparency. In the researchers’ experiments, the three programs’ error rates in determining the gender of light-skinned men were never worse than 0.8%.

When it comes to processing power, the human brain just can’t be beat. Packed within the squishy, football-sized organ are somewhere around 100 billion neurons. At any given moment, a single neuron can relay instructions to thousands of other neurons via synapses — the spaces between neurons, across which neurotransmitters are exchanged.

Before coming to MIT, Jeff Orkin SM ’07, PhD ’13 spent a decade building advanced, critically acclaimed AI for video games. While working on F.E.A.R., a survival-horror first-person shooter game, he developed AI that gave computer-controlled characters an unprecedented range of actions. Today, more than 10 years later, many video game enthusiasts still consider the game’s AI unmatched, even by modern standards.

It’s been another eventful 12 months in the world of technology. Further ground has been broken in the journey towards autonomous driving and the integration of technology in the healthcare sector. Plus the continued drive for smaller size, lower power devices. This has also been met with further concerns around data security as more and more ‘things’ around us become connected.

Neural networks, which learn to perform computational tasks by analysing huge sets of training data, have been responsible for the most impressive recent advances in artificial intelligence, including speech-recognition and automatic-translation systems. During training, however, a neural net continually adjusts its internal settings in ways that even its creators can’t interpret. Much recent work in computer science has focused on clever techniques for determining just how neural nets do what they do.

In a traditional computer, a microprocessor is mounted on a “package,” a small circuit board with a grid of electrical leads on its bottom. The package snaps into the computer’s motherboard, and data travels between the processor and the computer’s main memory bank through the leads. As processors’ transistor counts have gone up, the relatively slow connection between the processor and main memory has become the chief impediment to improving computers’ performance.

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